Citation

Eirena, Aina and Packier Mohammad, Nathar Shah (2025) Generative AI in University Customer Service: A Comprehensive Framework for Enhancing Efficiency and Experience. Journal of Logistics, Informatics and Service Science, 12 (7). pp. 56-67. ISSN 2409-2665

Text Vol.12.No.7.04.docx.pdf - Published Version Restricted to Repository staff only Download (266kB)

Abstract

This research investigates the application of Generative Artificial Intelligence (GAI) in university customer service, specifically to automate the real-time answering of student questions. The study assesses the effectiveness of GAI solutions, including large language models (LLMs) and hybrid systems, for enhancing the responsiveness, scalability, and personalization of Student Support Systems (SSSs), while also examining the ethical and operational limits of these technologies. Through a systematic literature review, the study synthesizes key trends and identifies a gap in domain-specific, theoretically-grounded studies. An exploratory comparative experiment is then presented using a manually constructed dataset of 200 student inquiries to evaluate six different models across quantitative metrics (accuracy, F1-score, latency) and qualitative dimensions (response quality, empathy). The findings validate the potential of GAI, with GPT-4 significantly surpassing traditional and deep learning models in accuracy, F1-score, and user-perceived empathy. However, the study also highlights critical challenges, including hallucinations, bias, and privacy concerns, which necessitate transparent, secure, and inclusive application. The findings, interpreted through the lens of Service Quality Theory, validate GAI's potential to revolutionize university service provision while also drawing attention to the requirements for human oversight and continuous refinement.

Item Type:	Article
Uncontrolled Keywords:	Customer service, generative AI
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	22 Dec 2025 05:31
Last Modified:	26 Dec 2025 04:30
URII:	http://shdl.mmu.edu.my/id/eprint/15104

Downloads

Edit (login required)